1. Technical Field
The present disclosure relates to a method and system of detecting an object of interest in an input image, and more particularly, to a method and system for detecting the left ventricle of the heart in a 2D/3D echocardiogram.
2. Discussion of Related Art
A doctor can diagnose the health of a patient by examining their medical images and observing unique characteristics of particular objects of interest within those images. For example, a cardiologist needs to be able quickly locate the left ventricle (LV) of the heart in a 2D/3D echocardiogram image to diagnose the health of a patient's heart. One can cut the 3D echocardiogram into several standard 2D slices such as apical four chamber, apical two chamber, and parasternal short axis views. However, such a naive slice cuffing renders planes that have meaningless interpretation because the parameters that represent the LV are unknown. The parameters, such as features, size, location, and orientation of the left ventricle within the echocardiogram image can vary based on several factors. For example, the features, size, location, and orientation of the left ventricle within a heart differs in each patient, the type of ultrasound machine used can vary, and a technician can aim an ultrasound scanning device at the heart of a patient at various angles and locations.
An object detection system can aid a doctor in the quick detection of an object of interest (e.g., the left ventricle) within a medical image, resulting in a more efficient and accurate diagnosis. A current object detector trains offline, a binary classifier that differentiates the object of interest between the background of the input image, The object detector then performs an online search to exhaustively slide a scanning window on the input image to detect instances of the object of interest. It also uses the so-called integral image for efficient evaluation of the detector. The trained classifier may be denoted by posterior probability ρ(O|I). The posterior probability ρ(O|I) of an uncertain proposition is its conditional probability taking empirical data into account. The online search can be mathematically represented by one of the following two equations:find{θ:ρ(O|I(θ)>0.5;θεΘ},  (1){circumflex over (θ)}=arg maxθεΘρ(O|I(θ)),  (2)where I(θ) is an image patch parameterized by θ and Θ is the parameter space where the search is conducted. One object of interest can be detected in equation (2), while several objects of interest can be detected by equation (1).
The efficiency of the object detector is affected by variations in the scaling and rotation of the object of interest. For example, the left ventricle in one patient can be much larger and oriented at a different angle as compared to another patient. There are three approaches in the literature to deal with rotation variation: the first is to rotate the image, the second is to train different classifiers for different angles or scales, and the third is to train one classifier by pooling together data from all orientations.
In the first approach, for each sampled 3D image, one rotates the volume of the 3D image and computes an integral volume from the rotated volume. For each sampled 2D image, one rotates the 2D image and computes an integral image from the rotated 2D image. However, volume/image rotation and integral volume/image computation are time consuming since they are linearly dependent on the number pixels of the input image.
In the second approach, only one integral image/volume of the input image is computed. Classifiers for only a sparse set of orientations are then tested to meet real time constraints. However, since an object of interest can be at various orientations and scales, it can be difficult to build an accurate object detector without sacrificing speed.
In the third approach, the variation introduced by the rotation is treated as an intra-class variation. However, such treatment significantly complicates appearance variation, rendering a more complex classifier and making it difficult to recover rotation information.
Thus, there is a need for method and system for detecting an object of interest that can quickly estimate the pose of the object of interest (e.g., the left ventricle) at an early stage of image detection. The estimated pose can then be used to simplify the later more complex stages of image detection.